The wisdom of crowds can often provide better decision-making capabilities than the best guesses of experts. Automated systems have therefore evolved to offer predictive tools to institutional clients based upon the analysis of a well-chosen set of individuals; these prediction services in turn are sometimes implemented as “prediction markets,” i.e., as a system or game that attempts to measure the conviction of knowledgeable individuals with something at stake. A prediction market typically treats events being predicted as a financial market and allows users to trade “stocks” representing event outcomes. Because users can place varying bets on outcomes, the magnitude and type of bet made provides some measure of confidence of the user's belief. These “financial games” can involve “real money” but typically simply feature points or “play money” given to a number of users, with some means of rewarding those individuals that predict correct event outcomes. Prediction markets can be complex, sometimes being managed under the control of sophisticated software. Prediction markets and associated forecasting can be applied in a wide-variety of applications, from predicting the outcome of real-life events such as elections or sporting events, to corporate forecasting, and even to Las Vegas-style gaming. Other applications of this technology also exist.
Not surprisingly, the accuracy of forecasts are heavily-dependent on the nature of the underlying data. If the ability to support different possible event outcomes and express varying levels of confidence is sufficiently robust, the resultant forecasts can be quite accurate and provide flexibility to adjustment of underlying conditions. By contrast, if confidence cannot be easily expressed, and if the choice of outcome possibility is not sufficiently flexible, the forecast can both be inaccurate and provide little flexibility to changes in assumptions.
What is needed is a system and related method that can be used to provide additional capabilities in terms of collecting robust data and applying that data in a meaningful manner to a forecasting or similar system. The present invention addresses these needs and provides related advantages.